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  DeepLabCut: markerless pose estimation of user-defined body parts with deep learning

Mathis, A., Mamidanna, P., Cury, A., Abe, T., Murthy, V., Mathis, M., et al. (2018). DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nature Neuroscience, 21(9), 1281-1289. doi:10.1038/s41593-018-0209-y.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0001-F746-A Version Permalink: http://hdl.handle.net/21.11116/0000-0002-04B4-E
Genre: Journal Article

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 Creators:
Mathis, A, Author
Mamidanna, P, Author
Cury, AM, Author
Abe, T, Author
Murthy, VN, Author
Mathis, MW, Author
Bethge, M1, 2, Author              
Affiliations:
1Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497805              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: Quantifying behavior is crucial for many applications in neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis can be highly time consuming. In motor control studies, humans or other animals are often marked with reflective markers to assist with computer-based tracking, but markers are intrusive, and the number and location of the markers must be determined a priori. Here we present an efficient method for markerless pose estimation based on transfer learning with deep neural networks that achieves excellent results with minimal training data. We demonstrate the versatility of this framework by tracking various body parts in multiple species across a broad collection of behaviors. Remarkably, even when only a small number of frames are labeled (~200), the algorithm achieves excellent tracking performance on test frames that is comparable to human accuracy.

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 Dates: 2018-082018-09
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.1038/s41593-018-0209-y
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Title: Nature Neuroscience
  Other : Nat. Neurosci.
Source Genre: Journal
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Publ. Info: New York, NY : Nature America Inc.
Pages: - Volume / Issue: 21 (9) Sequence Number: - Start / End Page: 1281 - 1289 Identifier: ISSN: 1097-6256
CoNE: /journals/resource/954925610931